No Association Between Antidepressant Efficacy and rs28365143 in Corticotropin-Releasing Hormone Binding Protein in a Large Meta-Analysis
Publication: American Journal of Psychiatry
To the Editor: In the March 2018 issue of the Journal, O’Connell et al. (1) report an association between rs28365143, which lies in the corticotropin-releasing hormone binding protein (CRHBP) gene, and efficacy of antidepressant treatment in major depressive disorder. The association was found only in patients treated with the selective serotonin reuptake inhibitors (SSRIs) escitalopram and sertraline, and not with venlafaxine. Under a dominant genetic model, patients carrying the minor allele A showed worse treatment outcomes compared with GG homozygotes. The article uses 636 participants from the International Study to Predict Optimized Treatment in Depression (iSPOT-D), with replication in 141 participants from the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study. The article does not report results from the genome-wide meta-analysis of the Genome-Based Therapeutic Drugs for Depression (GENDEP) project, the Munich Antidepressant Response Signature (MARS) project, and the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study (2,256 participants) (2). This meta-analysis shows no evidence of association between rs28365143 and antidepressant remission (p=0.70) or symptom improvement (p=0.54) (3).
The study from O’Connell et al. highlights the challenges in analyzing candidate gene studies with participants across ethnic groups. Studies of antidepressant response are particularly susceptible to population stratification because response differs by ethnicity (for example, African Americans had substantially lower remission rates than Caucasians in the STAR*D study [4]). Combined with allele frequency differences across populations, ethnic differences can lead to artifactual results that reflect genetic ancestry and not genetic association. Adding ancestry-informative principal components as covariates in the regression analysis protects against false positive results, but this was not possible with the candidate variant genotyping in the iSPOT-D. The authors performed a sensitivity test showing that the effect size for rs28365143 was similar in Caucasian iSPOT-D participants (60.1%) and non-Caucasians (39.9%; see Table S4 in the data supplement accompanying the online edition of the original article). This may not be a sufficient control because the non-Caucasian group included black, Asian, other, and missing races (see Table 2 in the article). In addition, the frequency of the rs28365143 A allele differs by ancestry (3.5% in European populations; 18.4% in African populations) and is missing in East Asian populations (5).
As a direct test for replication of the study findings, we performed a meta-analysis of rs28365143 association with SSRI remission and symptom improvement in 3,065 participants with major depressive disorder from seven samples (6–9). In our analysis, rs28365143 was imputed according to methods previously described (imputation quality was good in all samples), and the analysis used an additive model with principal components (10). All patients were Caucasian and were treated with SSRIs (citalopram, escitalopram, fluoxetine, paroxetine, and sertraline). Alpha was set at 0.05, and our sample size provided adequate power (>0.80) to detect an odds ratio of 0.31 corresponding to the effect reported by O’Connell et al. (11). We found no significant association of rs28365143 with symptom improvement (p=0.15, beta=−0.09, 95% CI=−0.22 to 0.03) or remission (p=0.11, odds ratio=0.79, 95% CI=0.60–1.06), although the direction of effect was the same as found by O’Connell et al. Results using a dominant model were similar to the additive model probably because the low minor allele frequency makes these models equivalent.
Independent replication represents a fundamental step in pharmacogenetics. Our negative meta-analysis in larger samples cautions against accepting results from small selective replication instead of using large, available data sets.
Acknowledgments
The authors thank NIMH for providing access to the STAR*D sample, where data and biomaterials were obtained from limited-access data sets.
References
1.
O’Connell CP, Goldstein-Piekarski AN, Nemeroff CB, et al: Antidepressant outcomes predicted by genetic variation in corticotropin-releasing hormone binding protein. Am J Psychiatry 2018; 175:251–261
2.
GENDEP Investigators; MARS Investigators; STAR*D Investigators: Common genetic variation and antidepressant efficacy in major depressive disorder: a meta-analysis of three genome-wide pharmacogenetic studies. Am J Psychiatry 2013; 170:207–217
3.
Broad Institute: Ricopili: antidepressant efficacy in major depressive disorder. https://data.broadinstitute.org/mpg/ricopili/ (Accessed Jan 10, 2018)
4.
Trivedi MH, Rush AJ, Wisniewski SR, et al: Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: implications for clinical practice. Am J Psychiatry 2006; 163:28–40
5.
1000 Genomes Project Phase 3: rs28365143 allele frequencies. http://www.ensembl.org/Homo_sapiens/Variation/Population?db=core;r=5:76951761-76952761;v=rs28365143;vdb=variation;vf=8792695(Accessed Jan 10, 2018)
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Garriock HA, Kraft JB, Shyn SI, et al: A genomewide association study of citalopram response in major depressive disorder. Biol Psychiatry 2010; 67:133–138
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Uher R, Perroud N, Ng MY, et al: Genome-wide pharmacogenetics of antidepressant response in the GENDEP project. Am J Psychiatry 2010; 167:555–564
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Tansey KE, Guipponi M, Perroud N, et al: Genetic predictors of response to serotonergic and noradrenergic antidepressants in major depressive disorder: a genome-wide analysis of individual-level data and a meta-analysis. PLoS Med 2012; 9:e1001326
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Ji Y, Biernacka JM, Hebbring S, et al: Pharmacogenomics of selective serotonin reuptake inhibitor treatment for major depressive disorder: genome-wide associations and functional genomics. Pharmacogenomics J 2013; 13:456–463
10.
Fabbri C, Tansey KE, Perlis RH, et al: New insights into the pharmacogenomics of antidepressant response from the GENDEP and STAR*D studies: rare variant analysis and high-density imputation. Pharmacogenomics J (Epub ahead of print, Nov 21, 2017)
11.
Faul F, Erdfelder E, Lang A-G, et al: G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods 2007; 39:175–191
Information & Authors
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Accepted: April 2018
Published online: 1 June 2018
Published in print: June 01, 2018
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The STAR*D study was supported by NIMH Contract N01MH90003 to the University of Texas Southwestern Medical Center (ClinicalTrials.gov identifier: NCT00021528). The GENDEP project was supported by a European Commission Framework 6 grant (contract reference: LSHB-CT-2003-503428). The Medical Research Council, United Kingdom, and GlaxoSmithKline (G0701420) provided support for genotyping. The NEWMEDS study was funded by the Innovative Medicine Initiative Joint Undertaking (IMI-JU) under grant agreement 115008, resources of which are from in-kind contributions from the European Union and the European Federation of Pharmaceutical Industries and Associations (EFPIA), as well as financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013). EFPIA members Pfizer, GlaxoSmithKline, and F. Hoffmann–La Roche have contributed work and samples to the project presented here. The funders had no role in study design, data collection and analysis, the decision to publish, or in the preparation of the manuscript. The Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) data set used for the analyses described in this letter was obtained from the dbGaP (study accession phs000670.v1.p1). The PGRN-AMPS was supported in part by NIH grants RO1 GM28157, U19 GM61388 (The Pharmacogenomics Research Network), U01 HG005137, R01 CA138461, and P20 1P20AA017830-01 (The Mayo Clinic Center for Individualized Treatment of Alcohol Dependence) and by a PhRMA Foundation Center of Excellence in Clinical Pharmacology award.Dr. Perlis has received fees for service on scientific advisory boards or consulting with Genomind, Psy Therapeutics, and RID Ventures; he holds equity in Psy Therapeutics; and he receives royalties from Massachusetts General Hospital. The other authors report no financial relationships with commercial interests.
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